[1]} combine Deep Kernel Learning [2]} framework and GPs by using NNs to learn low dimensional representation where the GP model is jointly trained, resulting in a method called Deterministic Uncertainty Quantification (DUQ).
More recently, [3]} showed that the DUQ method based on DKL can map OOD data close to training data samples, referred as “feature collapse”.
The authors thus propose the Deterministic Uncertainty Estimation (DUE) which in addition to DUQ method ensures that the encoder mapping is bi-Lipschitz.
It would be interesting to explore if these methods perform well on similar OOD experiments as those considered in this work.
